Parameter Sensitivity and Predictive Uncertainty in a New Water Quality Model, ${\mathrm{Q}}^2$

A new dynamic model of water quality, ${\mathrm{Q}}^2$ , has recently been developed, capable of simulating large branched river systems. This paper describes the application of a generalized sensitivity analysis (GSA) to ${\mathrm{Q}}^2$ for single reaches of the River Thames in southern England. Focusing on the simulation of dissolved oxygen (DO) (since this may be regarded as a proxy for the overall health of a river); the GSA is used to identify key parameters controlling model behavior and provide a probabilistic procedure for model calibration. It is shown that, in the River Thames at least, it is more important to obtain high quality forcing functions than to obtain improved parameter estimates once approximate values have been estimated. Furthermore, there is a need to ensure reasonable simulation of a range of water quality determinands, since a focus only on DO increases predictive uncertainty in the DO simulations. The ${\mathrm{Q}}^2$ model has been applied here to the River Thames, but it has a broad utility for evaluating other systems in Europe and around the world.